rr # Libraries library(ggplot2) library(dplyr) library(forcats) library(ggpubr) library(cowplot)

rr mynamestheme <- theme_bw()+ theme(plot.title = element_text(family = , face = , size = (25)), legend.title = element_text(colour = , face = , family = ,size = (20)), legend.text = element_text(face = , colour=,family = ,size = (18) ), axis.title = element_text(family = , face = ,size = (20), colour = ), axis.text = element_text(family = ,face = , colour = , size = (18)))

rr #P value correction #AZM and Placebo at all visits Aitchison only

pa<-c(0.76, 0.001,0.07) p.adjust(pa, method=, n=length(pa)) #[1] 0.760 0.003 0.140

#P value correction #AZM and Placebo at all visits Bray-Curtis only

pb<-c(0.97, 0.001,0.10) p.adjust(pb, method=, n=length(pb)) #[1] 0.970 0.003 0.200

#Baseline

rr newdat0<-read.csv(/t0fg_CLR_euclidean1.csv.csv, stringsAsFactors = TRUE) colnames(newdat0) str(newdat0)

Renaming factor levels dplyr

newdat0\(Group <- recode_factor(newdat0\)Group, AZM= \1 , Placebo=\2, Bewtween_group_matrix = \3  ) str(newdat0)

p values

rr pvla0 <- data.frame( group1 = \1, group2 = \3, label = *p = 0.76, y.position = 140 )

rr ba0<-ggplot(newdat0, aes(y=Values, x=Group, colour=Group)) + geom_violin(aes(fill=Group,colour=NULL))+geom_boxplot(aes(fill=Group, colour=NULL), width=.1, fill=, outlier.colour = )+coord_cartesian(ylim = c(0, 150))+ labs( x=NULL, y=distance)+mynamestheme+stat_pvalue_manual(pvla0, label = ,size = 7, tip.length = 0)+theme(legend.position = )+ scale_fill_manual(values=c(#A087BC, #FFF468, ))+ scale_x_discrete(labels=c(, , & Placebo)) ba0

#Baseline - Bray-Curtis

rr newdat0b<-read.csv(/t0fg_bray1.csv.csv, stringsAsFactors = TRUE) colnames(newdat0b) str(newdat0b)

Renaming factor levels dplyr

newdat0b\(Group <- recode_factor(newdat0b\)Group, AZM= \1 , Placebo=\2, Bewtween_group_matrix = \3  ) str(newdat0b)

p values

rr pvla0b <- data.frame( group1 = \1, group2 = \3, label = *p = 0.97, y.position = 1.08 )

rr ba0b<-ggplot(newdat0b, aes(y=Values, x=Group, colour=Group)) + geom_violin(aes(fill=Group,colour=NULL))+geom_boxplot(aes(fill=Group, colour=NULL), width=.1, fill=, outlier.colour = )+coord_cartesian(ylim = c(0, 1.2))+ labs( x=NULL, y=-Curtis distance)+mynamestheme+stat_pvalue_manual(pvla0b, label = ,size = 7, tip.length = 0)+theme(legend.position = )+ scale_fill_manual(values=c(#A087BC, #FFF468, ))+ scale_x_discrete(labels=c(, , & Placebo)) ba0b

rr #Combine the two plots m1<-cowplot::plot_grid(ba0, ba0b, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c(, ), hjust =-0.9, label_size = 22, label_fontfamily = , label_fontface = , label_colour = blue) m1

#Week 48

rr newdat12<-read.csv(/t12fg_CLR_euclidean1.csv.csv, stringsAsFactors = TRUE) colnames(newdat12) str(newdat12)

Renaming factor levels dplyr

newdat12\(Group <- recode_factor(newdat12\)Group, AZM= \1 , Placebo=\2, Bewtween_group_matrix = \3  ) str(newdat12)

p values

rr pvla12 <- data.frame( group1 = \1, group2 = \3, label = *p = 0.003, y.position = 135 )

rr ba12<-ggplot(newdat12, aes(y=Values, x=Group, colour=Group)) + geom_violin(aes(fill=Group,colour=NULL))+geom_boxplot(aes(fill=Group, colour=NULL), width=.1, fill=, outlier.colour = )+coord_cartesian(ylim = c(0, 150))+ labs( x=NULL, y=distance)+mynamestheme+stat_pvalue_manual(pvla12, label = ,size = 7, tip.length = 0)+theme(legend.position = )+ scale_fill_manual(values=c(#A087BC, #FFF468, ))+ scale_x_discrete(labels=c(, , & Placebo)) ba12

#Week 48 - Bray-Curtis

rr newdat12b<-read.csv(/t12fg_bray1.csv.csv, stringsAsFactors = TRUE) colnames(newdat12b) str(newdat12b)

Renaming factor levels dplyr

newdat12b\(Group <- recode_factor(newdat12b\)Group, AZM= \1 , Placebo=\2, Bewtween_group_matrix = \3  ) str(newdat12b)

p values

rr pvla12b <- data.frame( group1 = \1, group2 = \3, label = *p = 0.003, y.position = 1.08 )

rr ba12b<-ggplot(newdat12b, aes(y=Values, x=Group, colour=Group)) + geom_violin(aes(fill=Group,colour=NULL))+geom_boxplot(aes(fill=Group, colour=NULL), width=.1, fill=, outlier.colour = )+coord_cartesian(ylim = c(0, 1.2))+ labs( x=NULL, y=-Curtis distance)+mynamestheme+stat_pvalue_manual(pvla12b, label = ,size = 7, tip.length = 0)+theme(legend.position = )+ scale_fill_manual(values=c(#A087BC, #FFF468, ))+ scale_x_discrete(labels=c(, , & Placebo)) ba12b

rr #Combine the two plots m12<-cowplot::plot_grid(ba12, ba12b, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c(, ), hjust =-0.9, label_size = 22, label_fontfamily = , label_fontface = , label_colour = blue) m12

#Week 72

rr newdat2<-read.csv(/t18fg_CLR_euclidean1.csv.csv, stringsAsFactors = TRUE) colnames(newdat2) str(newdat2)

Renaming factor levels dplyr

newdat2\(Group <- recode_factor(newdat2\)Group, AZM= \1 , Placebo=\2, Bewtween_group_matrix = \3  ) str(newdat2)

p values

rr pvla <- data.frame( group1 = \1, group2 = \3, label = *p = 0.14, y.position = 135 )

rr ba18<-ggplot(newdat2, aes(y=Values, x=Group, colour=Group)) + geom_violin(aes(fill=Group,colour=NULL))+geom_boxplot(aes(fill=Group, colour=NULL), width=.1, fill=, outlier.colour = )+coord_cartesian(ylim = c(0, 150))+ labs( x=NULL, y=distance)+mynamestheme+stat_pvalue_manual(pvla, label = ,size = 7, tip.length = 0)+theme(legend.position = )+ scale_fill_manual(values=c(#A087BC, #FFF468, ))+ scale_x_discrete(labels=c(, , & Placebo)) ba18

#Week 72- Bray-Curtis

rr newdat18b<-read.csv(/t18fg_bray1.csv.csv, stringsAsFactors = TRUE) colnames(newdat18b) str(newdat18b)

Renaming factor levels dplyr

newdat18b\(Group <- recode_factor(newdat18b\)Group, AZM= \1 , Placebo=\2, Bewtween_group_matrix = \3  ) str(newdat18b)

p values

rr pvla18b <- data.frame( group1 = \1, group2 = \3, label = *p = 0.20, y.position = 1.08 )

rr ba18b<-ggplot(newdat18b, aes(y=Values, x=Group, colour=Group)) + geom_violin(aes(fill=Group,colour=NULL))+geom_boxplot(aes(fill=Group, colour=NULL), width=.1, fill=, outlier.colour = )+coord_cartesian(ylim = c(0, 1.2))+ labs( x=NULL, y=-Curtis distance)+mynamestheme+stat_pvalue_manual(pvla18b, label = ,size = 7, tip.length = 0)+theme(legend.position = )+ scale_fill_manual(values=c(#A087BC, #FFF468, ))+ scale_x_discrete(labels=c(, , & Placebo)) ba18b

rr #Combine the two plots m18<-cowplot::plot_grid(ba18, ba18b, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c(, ), hjust =-0.9, label_size = 22, label_fontfamily = , label_fontface = , label_colour = blue) m18

#Combine all- AZM and placebo

rr all<-cowplot::plot_grid(m1, m12, m18, nrow = 3, ncol = 1, scale = .8, vjust=c(2.0,1.2,1.2), hjust=c(-5.3,-5.5,-5.5), labels = c(, 48, 72), label_size = 30,label_fontfamily = ) all

ggsave(_all_labels13thDec2021.pdf, all, width = 50, height = 50, units = )

#P value correction for multiple testing AZM only

rr #P value correction #AZM at all visits Aitchison only

pa<-c(0.002, 0.002,0.56) p.adjust(pa, method=, n=length(pa)) #[1] 0.004 0.004 0.560

#P value correction #AZM at all visits Bray-Curtis only

pb<-c(0.001, 0.001,0.03) p.adjust(pb, method=, n=length(pb)) #[1] 0.002 0.002 0.030

#—AZM only—– #Baseline and 48 weeks

rr #—————AZM baseline and 48 weeks- Aitchison——————————- datazm012A<-read.csv(/azm012pfg_CLR_euclidean1.csv.csv, stringsAsFactors = TRUE, row.names = 1) colnames(datazm012A)

[1] \Visit\  \Values\ \Arm\   

rr str(datazm012A)

'data.frame':   42486 obs. of  3 variables:
 $ Visit : Factor w/ 3 levels \0m\,\12m\,\Bewtween_group_matrix\: 1 1 1 1 1 1 1 1 1 1 ...
 $ Values: num  54.5 43.1 31 38.7 30.7 ...
 $ Arm   : Factor w/ 1 level \AZM\: 1 1 1 1 1 1 1 1 1 1 ...

rr summary(datazm012A)

                   Visit           Values         Arm       
 0m                   :10585   Min.   :  8.352   AZM:42486  
 12m                  :10585   1st Qu.: 33.959              
 Bewtween_group_matrix:21316   Median : 41.201              
                               Mean   : 43.597              
                               3rd Qu.: 50.644              
                               Max.   :126.538              

rr #Manually assigning p values pv012A <- data.frame( group1 = \0m, group2 = _group_matrix, label = *p = 0.004, y.position = 128 )

#Figure bc012A<-ggplot(datazm012A, aes(y=Values, x=Visit, colour=Visit)) + geom_violin(aes(fill=Visit,colour=NULL))+ geom_boxplot(aes(fill=Visit, colour=NULL), width=.1, fill=, outlier.colour = )+ coord_cartesian(ylim = c(0, 140))+ labs( x=NULL, y=distance)+ mynamestheme+ stat_pvalue_manual(pv012A, label = ,size = 7, tip.length = 0.0)+ scale_fill_manual(values=c(#E1D0FF, #8662BD, ))+ scale_x_discrete(labels=c(, 48, & Week 48))+ theme(legend.position = ) bc012A

rr

#Combine the two plots m012<-cowplot::plot_grid(bc012A, bc012, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c(, ), hjust =-0.9, label_size = 22, label_fontfamily = , label_fontface = , label_colour = blue) m012

ggsave(012AZM_beta13thDec.pdf, m012, width = 40, height = 25, units = )

#Week 48 and 72- AZM only

rr

#scale_fill_manual(values=c(#E1D0FF, #8662BD, #46019B))

#—————–AZM 48 and 72 weeks-Bray-Curtis———————– datazm1218<-read.csv(/azm1218pfg_bray1.csv.csv, stringsAsFactors = TRUE, row.names = 1) colnames(datazm1218)

[1] \Visit\  \Values\ \Arm\   

rr str(datazm1218)

'data.frame':   26796 obs. of  3 variables:
 $ Visit : Factor w/ 3 levels \12m\,\18m\,\Bewtween_group_matrix\: 1 1 1 1 1 1 1 1 1 1 ...
 $ Values: num  0.427 0.322 0.462 0.338 0.775 ...
 $ Arm   : Factor w/ 1 level \AZM\: 1 1 1 1 1 1 1 1 1 1 ...

rr summary(datazm1218)

                   Visit           Values         Arm       
 12m                  : 6670   Min.   :0.03149   AZM:26796  
 18m                  : 6670   1st Qu.:0.40606              
 Bewtween_group_matrix:13456   Median :0.50754              
                               Mean   :0.53163              
                               3rd Qu.:0.64083              
                               Max.   :0.99804              

rr #Manually assigning p values pv1218 <- data.frame( group1 = \12m, group2 = _group_matrix, label = *p = 0.002, y.position = 1.05 )

#Figure bc1218<-ggplot(datazm1218, aes(y=Values, x=Visit, colour=Visit)) + geom_violin(aes(fill=Visit,colour=NULL))+ geom_boxplot(aes(fill=Visit, colour=NULL), width=.1, fill=, outlier.colour = )+ coord_cartesian(ylim = c(0, 1.15))+ labs( x=NULL, y=-Curtis distance)+ mynamestheme+ stat_pvalue_manual(pv1218, label = ,size = 7, tip.length = 0.0)+ scale_fill_manual(values=c(#8662BD, #32224A, ))+ scale_x_discrete(labels=c(48, 72,48 & 72 ))+ theme(legend.position = ) bc1218

rr

#—————AZM 48 and 72 weeks- Aitchison——————————- datazm1218A<-read.csv(/azm1218pfg_CLR_euclidean1.csv.csv, stringsAsFactors = TRUE, row.names = 1) colnames(datazm1218A)

[1] \Visit\  \Values\ \Arm\   

rr str(datazm1218A)

'data.frame':   26796 obs. of  3 variables:
 $ Visit : Factor w/ 3 levels \12m\,\18m\,\Bewtween_group_matrix\: 1 1 1 1 1 1 1 1 1 1 ...
 $ Values: num  37.1 30.7 53.8 25.5 24.1 ...
 $ Arm   : Factor w/ 1 level \AZM\: 1 1 1 1 1 1 1 1 1 1 ...

rr summary(datazm1218A)

                   Visit           Values        Arm       
 12m                  : 6670   Min.   :  9.24   AZM:26796  
 18m                  : 6670   1st Qu.: 33.72              
 Bewtween_group_matrix:13456   Median : 40.79              
                               Mean   : 42.88              
                               3rd Qu.: 49.86              
                               Max.   :117.11              

rr #Manually assigning p values pv1218A <- data.frame( group1 = \12m, group2 = _group_matrix, label = *p = 0.004, y.position = 128 )

#Figure bc1218A<-ggplot(datazm1218A, aes(y=Values, x=Visit, colour=Visit)) + geom_violin(aes(fill=Visit,colour=NULL))+ geom_boxplot(aes(fill=Visit, colour=NULL), width=.1, fill=, outlier.colour = )+ coord_cartesian(ylim = c(0, 140))+ labs( x=NULL, y=distance)+ mynamestheme+ stat_pvalue_manual(pv1218A, label = ,size = 7, tip.length = 0.0)+ scale_fill_manual(values=c(#8662BD, #32224A, ))+ scale_x_discrete(labels=c(48, 72, 48 & 72))+ theme(legend.position = ) bc1218A

rr

#Combine the two plots m1218<-cowplot::plot_grid(bc1218A, bc1218, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c(, ), hjust =-0.9, label_size = 22, label_fontfamily = , label_fontface = , label_colour = blue) m1218

ggsave(1218AZM_beta13thDec2021.pdf, m1218, width = 40, height = 25, units = )

rr NA NA NA

rr

#scale_fill_manual(values=c(#E1D0FF, #8662BD, #46019B))

#—————–AZM 48 and 72 weeks-Bray-Curtis———————– datazm018<-read.csv(/azm018pfg_bray1.csv.csv, stringsAsFactors = TRUE, row.names = 1) colnames(datazm018)

[1] \Visit\  \Values\ \Arm\   

rr str(datazm018)

'data.frame':   26335 obs. of  3 variables:
 $ Visit : Factor w/ 3 levels \0m\,\18m\,\Bewtween_group_matrix\: 1 1 1 1 1 1 1 1 1 1 ...
 $ Values: num  0.818 0.376 0.436 0.572 0.479 ...
 $ Arm   : Factor w/ 1 level \AZM\: 1 1 1 1 1 1 1 1 1 1 ...

rr summary(datazm018)

                   Visit           Values         Arm       
 0m                   : 6555   Min.   :0.01698   AZM:26335  
 18m                  : 6555   1st Qu.:0.40672              
 Bewtween_group_matrix:13225   Median :0.52535              
                               Mean   :0.54428              
                               3rd Qu.:0.67601              
                               Max.   :0.99404              

rr #Manually assigning p values pv018 <- data.frame( group1 = \0m, group2 = _group_matrix, label = *p = 0.03, y.position = 1.05 )

#Figure bc018<-ggplot(datazm018, aes(y=Values, x=Visit, colour=Visit)) + geom_violin(aes(fill=Visit,colour=NULL))+ geom_boxplot(aes(fill=Visit, colour=NULL), width=.1, fill=, outlier.colour = )+ coord_cartesian(ylim = c(0, 1.15))+ labs( x=NULL, y=-Curtis distance)+ mynamestheme+ stat_pvalue_manual(pv018, label = ,size = 7, tip.length = 0.0)+ scale_fill_manual(values=c(#E1D0FF, #32224A, ))+ scale_x_discrete(labels=c(, 72, & Week 72))+ theme(legend.position = ) bc018

rr

#—————AZM 48 and 72 weeks- Aitchison——————————- datazm018A<-read.csv(/azm018pfg_CLR_euclidean1.csv.csv, stringsAsFactors = TRUE, row.names = 1) colnames(datazm018A)

[1] \Visit\  \Values\ \Arm\   

rr str(datazm018A)

'data.frame':   26335 obs. of  3 variables:
 $ Visit : Factor w/ 3 levels \0m\,\18m\,\Bewtween_group_matrix\: 1 1 1 1 1 1 1 1 1 1 ...
 $ Values: num  52.1 22.4 29.3 50.2 54 ...
 $ Arm   : Factor w/ 1 level \AZM\: 1 1 1 1 1 1 1 1 1 1 ...

rr summary(datazm018A)

                   Visit           Values         Arm       
 0m                   : 6555   Min.   :  9.529   AZM:26335  
 18m                  : 6555   1st Qu.: 33.216              
 Bewtween_group_matrix:13225   Median : 40.418              
                               Mean   : 42.936              
                               3rd Qu.: 49.553              
                               Max.   :123.869              

rr #Manually assigning p values pv018A <- data.frame( group1 = \0m, group2 = _group_matrix, label = *p = 0.56, y.position = 128 )

#Figure bc018A<-ggplot(datazm018A, aes(y=Values, x=Visit, colour=Visit)) + geom_violin(aes(fill=Visit,colour=NULL))+ geom_boxplot(aes(fill=Visit, colour=NULL), width=.1, fill=, outlier.colour = )+ coord_cartesian(ylim = c(0, 140))+ labs( x=NULL, y=distance)+ mynamestheme+ stat_pvalue_manual(pv018A, label = ,size = 7, tip.length = 0.0)+ scale_fill_manual(values=c(#E1D0FF, #32224A, ))+ scale_x_discrete(labels=c(, 72, & Week 72))+ theme(legend.position = ) bc018A

rr

#Combine the two plots m018<-cowplot::plot_grid(bc018A, bc018, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c(, ), hjust =-0.9, label_size = 22, label_fontfamily = , label_fontface = , label_colour = blue) m018

ggsave(018AZM_beta13thDec2021.pdf, m018, width = 40, height = 25, units = )

rr NA NA

#Combine all- AZM only

rr all_azm<-cowplot::plot_grid(m012, m1218, m018, nrow = 3, ncol = 1, scale = .8, vjust=c(2.0,1.2,1.2), hjust=c(-1.5,-1.95,-1.5), labels = c(vs Week 48 (AZM), 48 vs 72 (AZM), vs Week 72 (AZM)), label_size = 30,label_fontfamily = ) all_azm

ggsave(_ONLY_all_labels13thDec.pdf, all_azm, width = 50, height = 50, units = )

#—Placebo only—– #P value correction for multiple testing Placebo only only

rr #P value correction #AZM at all visits Aitchison only

pa<-c(0.33, 0.70,0.17) p.adjust(pa, method=, n=length(pa)) #[1] 0.66 0.70 0.51

#P value correction #AZM at all visits Bray-Curtis only

pb<-c(0.51, 0.33,0.28) p.adjust(pb, method=, n=length(pb)) #[1] 0.51 0.51 0.51

#Baseline and 48 weeks

rr #Placebo only

Warning messages:
1: In readChar(file, size, TRUE) : truncating string with embedded nuls
2: In readChar(file, size, TRUE) : truncating string with embedded nuls
3: In readChar(file, size, TRUE) : truncating string with embedded nuls
4: In readChar(file, size, TRUE) : truncating string with embedded nuls
5: In readChar(file, size, TRUE) : truncating string with embedded nuls
6: In readChar(file, size, TRUE) : truncating string with embedded nuls
7: In readChar(file, size, TRUE) : truncating string with embedded nuls
8: In readChar(file, size, TRUE) : truncating string with embedded nuls
9: In readChar(file, size, TRUE) : truncating string with embedded nuls
10: In readChar(file, size, TRUE) : truncating string with embedded nuls

rr #scale_fill_manual(values=c(#fff9ae, #dab600, #46019B))

#—————–Placebo baseline and 48 weeks -Bray- Curtis———————– datPlacebo012<-read.csv(/Placebo012pfg_bray1.csv.csv, stringsAsFactors = TRUE, row.names = 1) colnames(datPlacebo012)

[1] \Visit\  \Values\ \Arm\   

rr str(datPlacebo012)

'data.frame':   40755 obs. of  3 variables:
 $ Visit : Factor w/ 3 levels \0m\,\12m\,\Bewtween_group_matrix\: 1 1 1 1 1 1 1 1 1 1 ...
 $ Values: num  0.657 0.745 0.824 0.721 0.104 ...
 $ Arm   : Factor w/ 1 level \Placebo\: 1 1 1 1 1 1 1 1 1 1 ...

rr summary(datPlacebo012)

                   Visit           Values             Arm       
 0m                   :10153   Min.   :0.01376   Placebo:40755  
 12m                  :10153   1st Qu.:0.42545                  
 Bewtween_group_matrix:20449   Median :0.55729                  
                               Mean   :0.57597                  
                               3rd Qu.:0.73477                  
                               Max.   :1.00000                  

rr #Manually assigning p values pv012 <- data.frame( group1 = \0m, group2 = _group_matrix, label = *p = 0.51, y.position = 1.05 )

#Figure bc012<-ggplot(datPlacebo012, aes(y=Values, x=Visit, colour=Visit)) + geom_violin(aes(fill=Visit,colour=NULL))+ geom_boxplot(aes(fill=Visit, colour=NULL), width=.1, fill=, outlier.colour = )+ coord_cartesian(ylim = c(0, 1.15))+ labs( x=NULL, y=-Curtis distance)+ mynamestheme+ stat_pvalue_manual(pv012, label = ,size = 7,tip.length = 0.0)+ scale_fill_manual(values=c(#fff9ae, #dab600, ))+ scale_x_discrete(labels=c(, 48, & Week 48))+ theme(legend.position = ) bc012

rr #—————Placebo baseline and 48 weeks- Aitchison——————————- datPlacebo012A<-read.csv(/Placebo012pfg_CLR_euclidean1.csv.csv, stringsAsFactors = TRUE, row.names = 1) colnames(datPlacebo012A)

[1] \Visit\  \Values\ \Arm\   

rr str(datPlacebo012A)

'data.frame':   40755 obs. of  3 variables:
 $ Visit : Factor w/ 3 levels \0m\,\12m\,\Bewtween_group_matrix\: 1 1 1 1 1 1 1 1 1 1 ...
 $ Values: num  39.9 49 68.5 65.5 14.2 ...
 $ Arm   : Factor w/ 1 level \Placebo\: 1 1 1 1 1 1 1 1 1 1 ...

rr summary(datPlacebo012A)

                   Visit           Values             Arm       
 0m                   :10153   Min.   :  6.017   Placebo:40755  
 12m                  :10153   1st Qu.: 34.710                  
 Bewtween_group_matrix:20449   Median : 42.894                  
                               Mean   : 45.421                  
                               3rd Qu.: 53.316                  
                               Max.   :133.657                  

rr #Manually assigning p values pv012A <- data.frame( group1 = \0m, group2 = _group_matrix, label = *p = 0.66, y.position = 138 )

#Figure bc012A<-ggplot(datPlacebo012A, aes(y=Values, x=Visit, colour=Visit)) + geom_violin(aes(fill=Visit,colour=NULL))+ geom_boxplot(aes(fill=Visit, colour=NULL), width=.1, fill=, outlier.colour = )+ coord_cartesian(ylim = c(0, 150))+ labs( x=NULL, y=distance)+ mynamestheme+ stat_pvalue_manual(pv012A, label = ,size = 7, tip.length = 0.0)+ scale_fill_manual(values=c(#fff9ae, #dab600, ))+ scale_x_discrete(labels=c(, 48, & Week 48))+ theme(legend.position = ) bc012A

rr

#Combine the two plots m012<-cowplot::plot_grid(bc012A, bc012, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c(, ), hjust =-0.9, label_size = 22, label_fontfamily = , label_fontface = , label_colour = blue) m012

ggsave(012Placebo_beta13thDec2021.pdf, m012, width = 40, height = 25, units = )

#Week 48 and 72- Placebo only

rr

#scale_fill_manual(values=c(#fff9ae, #dab600, #46019B))

#—————–Placebo 48 and 72 weeks-Bray-Curtis———————– datPlacebo1218<-read.csv(/Placebo1218pfg_bray1.csv.csv, stringsAsFactors = TRUE, row.names = 1) colnames(datPlacebo1218)

[1] \Visit\  \Values\ \Arm\   

rr str(datPlacebo1218)

'data.frame':   24090 obs. of  3 variables:
 $ Visit : Factor w/ 3 levels \12m\,\18m\,\Bewtween_group_matrix\: 1 1 1 1 1 1 1 1 1 1 ...
 $ Values: num  0.603 0.516 0.281 0.979 0.445 ...
 $ Arm   : Factor w/ 1 level \Placebo\: 1 1 1 1 1 1 1 1 1 1 ...

rr summary(datPlacebo1218)

                   Visit           Values             Arm       
 12m                  : 5995   Min.   :0.01305   Placebo:24090  
 18m                  : 5995   1st Qu.:0.42965                  
 Bewtween_group_matrix:12100   Median :0.58323                  
                               Mean   :0.59007                  
                               3rd Qu.:0.75923                  
                               Max.   :1.00000                  

rr #Manually assigning p values pv1218 <- data.frame( group1 = \12m, group2 = _group_matrix, label = *p = 0.51, y.position = 1.05 )

#Figure bc1218<-ggplot(datPlacebo1218, aes(y=Values, x=Visit, colour=Visit)) + geom_violin(aes(fill=Visit,colour=NULL))+ geom_boxplot(aes(fill=Visit, colour=NULL), width=.1, fill=, outlier.colour = )+ coord_cartesian(ylim = c(0, 1.15))+ labs( x=NULL, y=-Curtis distance)+ mynamestheme+ stat_pvalue_manual(pv1218, label = ,size = 7, tip.length = 0.0)+ scale_fill_manual(values=c(#dab600, #554904, ))+ scale_x_discrete(labels=c(48, 72,48 & 72 ))+ theme(legend.position = ) bc1218

rr

#—————Placebo 48 and 72 weeks- Aitchison——————————- datPlacebo1218A<-read.csv(/Placebo1218pfg_CLR_euclidean1.csv.csv, stringsAsFactors = TRUE, row.names = 1) colnames(datPlacebo1218A)

[1] \Visit\  \Values\ \Arm\   

rr str(datPlacebo1218A)

'data.frame':   24090 obs. of  3 variables:
 $ Visit : Factor w/ 3 levels \12m\,\18m\,\Bewtween_group_matrix\: 1 1 1 1 1 1 1 1 1 1 ...
 $ Values: num  34.9 35.7 41.1 61.8 37.5 ...
 $ Arm   : Factor w/ 1 level \Placebo\: 1 1 1 1 1 1 1 1 1 1 ...

rr summary(datPlacebo1218A)

                   Visit           Values             Arm       
 12m                  : 5995   Min.   :  6.017   Placebo:24090  
 18m                  : 5995   1st Qu.: 33.316                  
 Bewtween_group_matrix:12100   Median : 40.742                  
                               Mean   : 42.860                  
                               3rd Qu.: 50.083                  
                               Max.   :117.128                  

rr #Manually assigning p values pv1218A <- data.frame( group1 = \12m, group2 = _group_matrix, label = *p = 0.70, y.position = 138 )

#Figure bc1218A<-ggplot(datPlacebo1218A, aes(y=Values, x=Visit, colour=Visit)) + geom_violin(aes(fill=Visit,colour=NULL))+ geom_boxplot(aes(fill=Visit, colour=NULL), width=.1, fill=, outlier.colour = )+ coord_cartesian(ylim = c(0, 150))+ labs( x=NULL, y=distance)+ mynamestheme+ stat_pvalue_manual(pv1218A, label = ,size = 7, tip.length = 0.0)+ scale_fill_manual(values=c(#dab600, #554904, ))+ scale_x_discrete(labels=c(48, 72, 48 & 72))+ theme(legend.position = ) bc1218A

rr

#Combine the two plots m1218<-cowplot::plot_grid(bc1218A, bc1218, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c(, ), hjust =-0.9, label_size = 22, label_fontfamily = , label_fontface = , label_colour = blue) m1218

ggsave(1218Placebo_beta13thDec2021.pdf, m1218, width = 40, height = 25, units = )

rr NA NA NA

rr

#scale_fill_manual(values=c(#fff9ae, #dab600, #46019B))

#—————–Placebo 48 and 72 weeks-Bray-Curtis———————– datPlacebo018<-read.csv(/Placebo018pfg_bray1.csv.csv, stringsAsFactors = TRUE, row.names = 1) colnames(datPlacebo018)

[1] \Visit\  \Values\ \Arm\   

rr str(datPlacebo018)

'data.frame':   24976 obs. of  3 variables:
 $ Visit : Factor w/ 3 levels \0m\,\18m\,\Bewtween_group_matrix\: 1 1 1 1 1 1 1 1 1 1 ...
 $ Values: num  0.657 0.581 0.481 0.498 0.703 ...
 $ Arm   : Factor w/ 1 level \Placebo\: 1 1 1 1 1 1 1 1 1 1 ...

rr summary(datPlacebo018)

                   Visit           Values             Arm       
 0m                   : 6216   Min.   :0.02508   Placebo:24976  
 18m                  : 6216   1st Qu.:0.43141                  
 Bewtween_group_matrix:12544   Median :0.56986                  
                               Mean   :0.57820                  
                               3rd Qu.:0.73365                  
                               Max.   :0.99906                  

rr #Manually assigning p values pv018 <- data.frame( group1 = \0m, group2 = _group_matrix, label = *p = 0.51, y.position = 1.05 )

#Figure bc018<-ggplot(datPlacebo018, aes(y=Values, x=Visit, colour=Visit)) + geom_violin(aes(fill=Visit,colour=NULL))+ geom_boxplot(aes(fill=Visit, colour=NULL), width=.1, fill=, outlier.colour = )+ coord_cartesian(ylim = c(0, 1.15))+ labs( x=NULL, y=-Curtis distance)+ mynamestheme+ stat_pvalue_manual(pv018, label = ,size = 7, tip.length = 0.0)+ scale_fill_manual(values=c(#fff9ae, #554904, ))+ scale_x_discrete(labels=c(, 72, & Week 72))+ theme(legend.position = ) bc018

rr

#—————Placebo 48 and 72 weeks- Aitchison——————————- datPlacebo018A<-read.csv(/Placebo018pfg_CLR_euclidean1.csv.csv, stringsAsFactors = TRUE, row.names = 1) colnames(datPlacebo018A)

[1] \Visit\  \Values\ \Arm\   

rr str(datPlacebo018A)

'data.frame':   24976 obs. of  3 variables:
 $ Visit : Factor w/ 3 levels \0m\,\18m\,\Bewtween_group_matrix\: 1 1 1 1 1 1 1 1 1 1 ...
 $ Values: num  39.9 56.7 35.1 58 64.5 ...
 $ Arm   : Factor w/ 1 level \Placebo\: 1 1 1 1 1 1 1 1 1 1 ...

rr summary(datPlacebo018A)

                   Visit           Values             Arm       
 0m                   : 6216   Min.   :  6.911   Placebo:24976  
 18m                  : 6216   1st Qu.: 34.608                  
 Bewtween_group_matrix:12544   Median : 42.537                  
                               Mean   : 44.729                  
                               3rd Qu.: 52.378                  
                               Max.   :120.050                  

rr #Manually assigning p values pv018A <- data.frame( group1 = \0m, group2 = _group_matrix, label = *p = 0.51, y.position = 138 )

#Figure bc018A<-ggplot(datPlacebo018A, aes(y=Values, x=Visit, colour=Visit)) + geom_violin(aes(fill=Visit,colour=NULL))+ geom_boxplot(aes(fill=Visit, colour=NULL), width=.1, fill=, outlier.colour = )+ coord_cartesian(ylim = c(0, 150))+ labs( x=NULL, y=distance)+ mynamestheme+ stat_pvalue_manual(pv018A, label = ,size = 7, tip.length = 0.0)+ scale_fill_manual(values=c(#fff9ae, #554904, ))+ scale_x_discrete(labels=c(, 72, & Week 72))+ theme(legend.position = ) bc018A

rr

#Combine the two plots m018<-cowplot::plot_grid(bc018A, bc018, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c(, ), hjust =-0.9, label_size = 22, label_fontfamily = , label_fontface = , label_colour = blue) m018

ggsave(018Placebo_beta13thDec2021.pdf, m018, width = 40, height = 25, units = )

rr NA NA

#Combine all- Placebo only

rr all_Placebo<-cowplot::plot_grid(m012, m1218, m018, nrow = 3, ncol = 1, scale = .9, vjust=c(1.1,1.2,1.2), hjust=c(-1.25,-1.69,-1.25), labels = c(vs Week 48 (Placebo), 48 vs 72 (Placebo), vs Week 72 (Placebo)), label_size = 30,label_fontfamily = ) all_Placebo

ggsave(_ONLY_all_labels13thDec2021.pdf, all_Placebo, width = 50, height = 50, units = )

---
title: "Between group beta diversity plots"
output: html_notebook
Author: Regina Esinam Abotsi, Department of Molecular and Cell Biology, University of Cape Town, South Africa.
---



```{r}
# Libraries
library(ggplot2)
library(dplyr)
library(forcats)
library(ggpubr)
library(cowplot)

```

```{r}
mynamestheme <- theme_bw()+ 
  theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (25)), 
        legend.title = element_text(colour = "black",  face = "bold", family = "Helvetica",size = (20)), 
        legend.text = element_text(face = "bold", colour="black",family = "Helvetica",size = (18) ),
        axis.title = element_text(family = "Helvetica", face = "bold",size = (20), colour = "black"),
        axis.text = element_text(family = "Helvetica",face = "bold", colour = "black", size = (18)))
```



```{r}
#P value correction
#AZM and Placebo at all visits Aitchison only 

pa<-c(0.76, 0.001,0.07)
p.adjust(pa, method="hochberg", n=length(pa))
#[1] 0.760 0.003 0.140

#P value correction
#AZM and Placebo at all visits Bray-Curtis only 

pb<-c(0.97, 0.001,0.10)
p.adjust(pb, method="hochberg", n=length(pb))
#[1] 0.970 0.003 0.200


```


#Baseline
```{r}
newdat0<-read.csv("data/t0fg_CLR_euclidean1.csv.csv", stringsAsFactors = TRUE)
colnames(newdat0)
str(newdat0)

# Renaming factor levels dplyr
newdat0$Group <- recode_factor(newdat0$Group, AZM= "1" , Placebo="2", Bewtween_group_matrix = "3"  )
str(newdat0)
```


p values
```{r}
pvla0 <- data.frame(
  group1 = "1",
  group2 = "3",
  label = "*p = 0.76",
  y.position = 140
)

```


```{r}
ba0<-ggplot(newdat0, aes(y=Values, x=Group, colour=Group)) + 
  geom_violin(aes(fill=Group,colour=NULL))+geom_boxplot(aes(fill=Group, colour=NULL), width=.1, fill="white", outlier.colour = "black")+coord_cartesian(ylim = c(0, 150))+
  labs( x=NULL,
       y="Aitchison distance")+mynamestheme+stat_pvalue_manual(pvla0, label = "label",size = 7, tip.length = 0)+theme(legend.position = "None")+
  scale_fill_manual(values=c("#A087BC", "#FFF468", "white"))+
  scale_x_discrete(labels=c("AZM", "Placebo", "AZM & Placebo"))
ba0

```

#Baseline - Bray-Curtis
```{r}
newdat0b<-read.csv("data/t0fg_bray1.csv.csv", stringsAsFactors = TRUE)
colnames(newdat0b)
str(newdat0b)

# Renaming factor levels dplyr
newdat0b$Group <- recode_factor(newdat0b$Group, AZM= "1" , Placebo="2", Bewtween_group_matrix = "3"  )
str(newdat0b)
```


p values
```{r}
pvla0b <- data.frame(
  group1 = "1",
  group2 = "3",
  label = "*p = 0.97",
  y.position = 1.08
)

```


```{r}
ba0b<-ggplot(newdat0b, aes(y=Values, x=Group, colour=Group)) + 
  geom_violin(aes(fill=Group,colour=NULL))+geom_boxplot(aes(fill=Group, colour=NULL), width=.1, fill="white", outlier.colour = "black")+coord_cartesian(ylim = c(0, 1.2))+
  labs( x=NULL,
       y="Bray-Curtis distance")+mynamestheme+stat_pvalue_manual(pvla0b, label = "label",size = 7, tip.length = 0)+theme(legend.position = "None")+
  scale_fill_manual(values=c("#A087BC", "#FFF468", "white"))+
  scale_x_discrete(labels=c("AZM", "Placebo", "AZM & Placebo"))
ba0b

```

```{r}
#Combine the two plots
m1<-cowplot::plot_grid(ba0, ba0b, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c("A", "B"), hjust =-0.9,  label_size = 22, label_fontfamily = "Helvetica", label_fontface = "bold", label_colour = "dark blue")
m1
```






#Week 48
```{r}
newdat12<-read.csv("data/t12fg_CLR_euclidean1.csv.csv", stringsAsFactors = TRUE)
colnames(newdat12)
str(newdat12)

# Renaming factor levels dplyr
newdat12$Group <- recode_factor(newdat12$Group, AZM= "1" , Placebo="2", Bewtween_group_matrix = "3"  )
str(newdat12)
```


p values
```{r}
pvla12 <- data.frame(
  group1 = "1",
  group2 = "3",
  label = "*p = 0.003",
  y.position = 135
)

```


```{r}
ba12<-ggplot(newdat12, aes(y=Values, x=Group, colour=Group)) + 
  geom_violin(aes(fill=Group,colour=NULL))+geom_boxplot(aes(fill=Group, colour=NULL), width=.1, fill="white", outlier.colour = "black")+coord_cartesian(ylim = c(0, 150))+
  labs( x=NULL,
       y="Aitchison distance")+mynamestheme+stat_pvalue_manual(pvla12, label = "label",size = 7, tip.length = 0)+theme(legend.position = "None")+
  scale_fill_manual(values=c("#A087BC", "#FFF468", "white"))+
  scale_x_discrete(labels=c("AZM", "Placebo", "AZM & Placebo"))
ba12

```



#Week 48 - Bray-Curtis
```{r}
newdat12b<-read.csv("data/t12fg_bray1.csv.csv", stringsAsFactors = TRUE)
colnames(newdat12b)
str(newdat12b)

# Renaming factor levels dplyr
newdat12b$Group <- recode_factor(newdat12b$Group, AZM= "1" , Placebo="2", Bewtween_group_matrix = "3"  )
str(newdat12b)
```


p values
```{r}
pvla12b <- data.frame(
  group1 = "1",
  group2 = "3",
  label = "*p = 0.003",
  y.position = 1.08
)

```


```{r}
ba12b<-ggplot(newdat12b, aes(y=Values, x=Group, colour=Group)) + 
  geom_violin(aes(fill=Group,colour=NULL))+geom_boxplot(aes(fill=Group, colour=NULL), width=.1, fill="white", outlier.colour = "black")+coord_cartesian(ylim = c(0, 1.2))+
  labs( x=NULL,
       y="Bray-Curtis distance")+mynamestheme+stat_pvalue_manual(pvla12b, label = "label",size = 7, tip.length = 0)+theme(legend.position = "None")+
  scale_fill_manual(values=c("#A087BC", "#FFF468", "white"))+
  scale_x_discrete(labels=c("AZM", "Placebo", "AZM & Placebo"))
ba12b

```

```{r}
#Combine the two plots
m12<-cowplot::plot_grid(ba12, ba12b, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c("C", "D"), hjust =-0.9,  label_size = 22, label_fontfamily = "Helvetica", label_fontface = "bold", label_colour = "dark blue")
m12
```



#Week 72

```{r}
newdat2<-read.csv("data/t18fg_CLR_euclidean1.csv.csv", stringsAsFactors = TRUE)
colnames(newdat2)
str(newdat2)

# Renaming factor levels dplyr
newdat2$Group <- recode_factor(newdat2$Group, AZM= "1" , Placebo="2", Bewtween_group_matrix = "3"  )
str(newdat2)
```


p values
```{r}
pvla <- data.frame(
  group1 = "1",
  group2 = "3",
  label = "*p = 0.14",
  y.position = 135
)

```


```{r}
ba18<-ggplot(newdat2, aes(y=Values, x=Group, colour=Group)) + 
  geom_violin(aes(fill=Group,colour=NULL))+geom_boxplot(aes(fill=Group, colour=NULL), width=.1, fill="white", outlier.colour = "black")+coord_cartesian(ylim = c(0, 150))+
  labs( x=NULL,
       y="Aitchison distance")+mynamestheme+stat_pvalue_manual(pvla, label = "label",size = 7, tip.length = 0)+theme(legend.position = "None")+
  scale_fill_manual(values=c("#A087BC", "#FFF468", "white"))+
  scale_x_discrete(labels=c("AZM", "Placebo", "AZM & Placebo"))
ba18

```



#Week 72- Bray-Curtis
```{r}
newdat18b<-read.csv("data/t18fg_bray1.csv.csv", stringsAsFactors = TRUE)
colnames(newdat18b)
str(newdat18b)

# Renaming factor levels dplyr
newdat18b$Group <- recode_factor(newdat18b$Group, AZM= "1" , Placebo="2", Bewtween_group_matrix = "3"  )
str(newdat18b)
```


p values
```{r}
pvla18b <- data.frame(
  group1 = "1",
  group2 = "3",
  label = "*p = 0.20",
  y.position = 1.08
)

```


```{r}
ba18b<-ggplot(newdat18b, aes(y=Values, x=Group, colour=Group)) + 
  geom_violin(aes(fill=Group,colour=NULL))+geom_boxplot(aes(fill=Group, colour=NULL), width=.1, fill="white", outlier.colour = "black")+coord_cartesian(ylim = c(0, 1.2))+
  labs( x=NULL,
       y="Bray-Curtis distance")+mynamestheme+stat_pvalue_manual(pvla18b, label = "label",size = 7, tip.length = 0)+theme(legend.position = "None")+
  scale_fill_manual(values=c("#A087BC", "#FFF468", "white"))+
  scale_x_discrete(labels=c("AZM", "Placebo", "AZM & Placebo"))
ba18b

```
```{r}
#Combine the two plots
m18<-cowplot::plot_grid(ba18, ba18b, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c("E", "F"), hjust =-0.9,  label_size = 22, label_fontfamily = "Helvetica", label_fontface = "bold", label_colour = "dark blue")
m18
```


#Combine all- AZM and placebo

```{r}
all<-cowplot::plot_grid(m1, m12, m18, nrow = 3, ncol = 1, scale = .8, vjust=c(2.0,1.2,1.2), hjust=c(-5.3,-5.5,-5.5), labels = c("Baseline", "Week 48", "Week 72"), label_size = 30,label_fontfamily = "Helvetica")
all


ggsave("Both_all_labels13thDec2021.pdf", all,  width = 50, height = 50, units = "cm")
```







#P value correction for multiple testing AZM only

```{r}
#P value correction
#AZM at all visits Aitchison only 

pa<-c(0.002, 0.002,0.56)
p.adjust(pa, method="hochberg", n=length(pa))
#[1] 0.004 0.004 0.560

#P value correction
#AZM at all visits Bray-Curtis only 

pb<-c(0.001, 0.001,0.03)
p.adjust(pb, method="hochberg", n=length(pb))
#[1] 0.002 0.002 0.030




```

#---AZM only-----
#Baseline and 48 weeks

```{r}
#AZM only


#scale_fill_manual(values=c("#b3cde3", "#EE82EE", "#46019B"))

#-----------------AZM baseline and 48 weeks -Bray- Curtis-----------------------
datazm012<-read.csv("data/azm012pfg_bray1.csv.csv", stringsAsFactors = TRUE, row.names = 1)
colnames(datazm012)
str(datazm012)
summary(datazm012)


#Manually assigning p values
pv012 <- data.frame(
  group1 = "0m",
  group2 = "Bewtween_group_matrix",
  label = "*p = 0.002",
  y.position = 1.05
)

#Figure
bc012<-ggplot(datazm012, aes(y=Values, x=Visit, colour=Visit)) + 
  geom_violin(aes(fill=Visit,colour=NULL))+
  geom_boxplot(aes(fill=Visit, colour=NULL), 
               width=.1, fill="white", outlier.colour = "black")+
  coord_cartesian(ylim = c(0, 1.15))+
  labs( x=NULL,
        y="Bray-Curtis distance")+
  mynamestheme+
  stat_pvalue_manual(pv012, label = "label",size = 7,tip.length = 0.0)+
  scale_fill_manual(values=c("#E1D0FF", "#8662BD", "white"))+
  scale_x_discrete(labels=c("Baseline", "Week 48", "Baseline & Week 48"))+
  theme(legend.position = "None")
bc012
```


```{r}
#---------------AZM baseline and 48 weeks- Aitchison-------------------------------
datazm012A<-read.csv("data/azm012pfg_CLR_euclidean1.csv.csv", stringsAsFactors = TRUE, row.names = 1)
colnames(datazm012A)
str(datazm012A)
summary(datazm012A)


#Manually assigning p values
pv012A <- data.frame(
  group1 = "0m",
  group2 = "Bewtween_group_matrix",
  label = "*p = 0.004",
  y.position = 128
)

#Figure
bc012A<-ggplot(datazm012A, aes(y=Values, x=Visit, colour=Visit)) + 
  geom_violin(aes(fill=Visit,colour=NULL))+
  geom_boxplot(aes(fill=Visit, colour=NULL), 
               width=.1, fill="white", outlier.colour = "black")+
  coord_cartesian(ylim = c(0, 140))+
  labs( x=NULL,
        y="Aitchison distance")+
  mynamestheme+
  stat_pvalue_manual(pv012A, label = "label",size = 7, tip.length = 0.0)+
  scale_fill_manual(values=c("#E1D0FF", "#8662BD", "white"))+
  scale_x_discrete(labels=c("Baseline", "Week 48", "Baseline & Week 48"))+
  theme(legend.position = "None")
bc012A


#Combine the two plots
m012<-cowplot::plot_grid(bc012A, bc012, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c("A", "B"), hjust =-0.9,  label_size = 22, label_fontfamily = "Helvetica", label_fontface = "bold", label_colour = "dark blue")
m012

ggsave("Month012AZM_beta13thDec.pdf", m012,  width = 40, height = 25, units = "cm")

```


#Week 48 and 72- AZM only
```{r}

#scale_fill_manual(values=c("#E1D0FF", "#8662BD", "#46019B"))

#-----------------AZM 48 and 72 weeks-Bray-Curtis-----------------------
datazm1218<-read.csv("data/azm1218pfg_bray1.csv.csv", stringsAsFactors = TRUE, row.names = 1)
colnames(datazm1218)
str(datazm1218)
summary(datazm1218)


#Manually assigning p values
pv1218 <- data.frame(
  group1 = "12m",
  group2 = "Bewtween_group_matrix",
  label = "*p = 0.002",
  y.position = 1.05
)

#Figure
bc1218<-ggplot(datazm1218, aes(y=Values, x=Visit, colour=Visit)) + 
  geom_violin(aes(fill=Visit,colour=NULL))+
  geom_boxplot(aes(fill=Visit, colour=NULL), 
               width=.1, fill="white", outlier.colour = "black")+
  coord_cartesian(ylim = c(0, 1.15))+
  labs( x=NULL,
        y="Bray-Curtis distance")+
  mynamestheme+
  stat_pvalue_manual(pv1218, label = "label",size = 7, tip.length = 0.0)+
  scale_fill_manual(values=c("#8662BD", "#32224A", "white"))+
  scale_x_discrete(labels=c("Week 48", "Week 72","Week 48 & 72" ))+
  theme(legend.position = "None")
bc1218


#---------------AZM 48 and 72 weeks- Aitchison-------------------------------
datazm1218A<-read.csv("data/azm1218pfg_CLR_euclidean1.csv.csv", stringsAsFactors = TRUE, row.names = 1)
colnames(datazm1218A)
str(datazm1218A)
summary(datazm1218A)


#Manually assigning p values
pv1218A <- data.frame(
  group1 = "12m",
  group2 = "Bewtween_group_matrix",
  label = "*p = 0.004",
  y.position = 128
)

#Figure
bc1218A<-ggplot(datazm1218A, aes(y=Values, x=Visit, colour=Visit)) + 
  geom_violin(aes(fill=Visit,colour=NULL))+
  geom_boxplot(aes(fill=Visit, colour=NULL), 
               width=.1, fill="white", outlier.colour = "black")+
  coord_cartesian(ylim = c(0, 140))+
  labs( x=NULL,
        y="Aitchison distance")+
  mynamestheme+
  stat_pvalue_manual(pv1218A, label = "label",size = 7, tip.length = 0.0)+
  scale_fill_manual(values=c("#8662BD", "#32224A", "white"))+
  scale_x_discrete(labels=c("Week 48", "Week 72", "Week 48 & 72"))+
  theme(legend.position = "None")
bc1218A


#Combine the two plots
m1218<-cowplot::plot_grid(bc1218A, bc1218, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c("C", "D"), hjust =-0.9,  label_size = 22, label_fontfamily = "Helvetica", label_fontface = "bold", label_colour = "dark blue")
m1218

ggsave("Month1218AZM_beta13thDec2021.pdf", m1218,  width = 40, height = 25, units = "cm")



```

```{r}

#scale_fill_manual(values=c("#E1D0FF", "#8662BD", "#46019B"))

#-----------------AZM 48 and 72 weeks-Bray-Curtis-----------------------
datazm018<-read.csv("data/azm018pfg_bray1.csv.csv", stringsAsFactors = TRUE, row.names = 1)
colnames(datazm018)
str(datazm018)
summary(datazm018)


#Manually assigning p values
pv018 <- data.frame(
  group1 = "0m",
  group2 = "Bewtween_group_matrix",
  label = "*p = 0.03",
  y.position = 1.05
)

#Figure
bc018<-ggplot(datazm018, aes(y=Values, x=Visit, colour=Visit)) + 
  geom_violin(aes(fill=Visit,colour=NULL))+
  geom_boxplot(aes(fill=Visit, colour=NULL), 
               width=.1, fill="white", outlier.colour = "black")+
  coord_cartesian(ylim = c(0, 1.15))+
  labs( x=NULL,
        y="Bray-Curtis distance")+
  mynamestheme+
  stat_pvalue_manual(pv018, label = "label",size = 7, tip.length = 0.0)+
  scale_fill_manual(values=c("#E1D0FF", "#32224A", "white"))+
  scale_x_discrete(labels=c("Baseline", "Week 72", "Baseline & Week 72"))+
  theme(legend.position = "None")
bc018


#---------------AZM 48 and 72 weeks- Aitchison-------------------------------
datazm018A<-read.csv("data/azm018pfg_CLR_euclidean1.csv.csv", stringsAsFactors = TRUE, row.names = 1)
colnames(datazm018A)
str(datazm018A)
summary(datazm018A)


#Manually assigning p values
pv018A <- data.frame(
  group1 = "0m",
  group2 = "Bewtween_group_matrix",
  label = "*p = 0.56",
  y.position = 128
)

#Figure
bc018A<-ggplot(datazm018A, aes(y=Values, x=Visit, colour=Visit)) + 
  geom_violin(aes(fill=Visit,colour=NULL))+
  geom_boxplot(aes(fill=Visit, colour=NULL), 
               width=.1, fill="white", outlier.colour = "black")+
  coord_cartesian(ylim = c(0, 140))+
  labs( x=NULL,
        y="Aitchison distance")+
  mynamestheme+
  stat_pvalue_manual(pv018A, label = "label",size = 7, tip.length = 0.0)+
  scale_fill_manual(values=c("#E1D0FF", "#32224A", "white"))+
  scale_x_discrete(labels=c("Baseline", "Week 72", "Baseline & Week 72"))+
  theme(legend.position = "None")
bc018A

#Combine the two plots
m018<-cowplot::plot_grid(bc018A, bc018, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c("E", "F"), hjust =-0.9,  label_size = 22, label_fontfamily = "Helvetica", label_fontface = "bold", label_colour = "dark blue")
m018

ggsave("Month018AZM_beta13thDec2021.pdf", m018,  width = 40, height = 25, units = "cm")


```


#Combine all- AZM only

```{r}
all_azm<-cowplot::plot_grid(m012, m1218, m018, nrow = 3, ncol = 1, scale = .8, vjust=c(2.0,1.2,1.2), hjust=c(-1.5,-1.95,-1.5), labels = c("Baseline vs Week 48 (AZM)", "Week 48 vs 72 (AZM)", "Baseline vs Week 72 (AZM)"), label_size = 30,label_fontfamily = "Helvetica")
all_azm


ggsave("AZM_ONLY_all_labels13thDec.pdf", all_azm,  width = 50, height = 50, units = "cm")
```




#---Placebo only-----
#P value correction for multiple testing Placebo only only

```{r}
#P value correction
#AZM at all visits Aitchison only 

pa<-c(0.33, 0.70,0.17)
p.adjust(pa, method="hochberg", n=length(pa))
#[1] 0.66 0.70 0.51

#P value correction
#AZM at all visits Bray-Curtis only 

pb<-c(0.51, 0.33,0.28)
p.adjust(pb, method="hochberg", n=length(pb))
#[1] 0.51 0.51 0.51




```

#Baseline and 48 weeks

```{r}
#Placebo only


#scale_fill_manual(values=c("#fff9ae", "#dab600", "#46019B"))

#-----------------Placebo baseline and 48 weeks -Bray- Curtis-----------------------
datPlacebo012<-read.csv("data/Placebo012pfg_bray1.csv.csv", stringsAsFactors = TRUE, row.names = 1)
colnames(datPlacebo012)
str(datPlacebo012)
summary(datPlacebo012)


#Manually assigning p values
pv012 <- data.frame(
  group1 = "0m",
  group2 = "Bewtween_group_matrix",
  label = "*p = 0.51",
  y.position = 1.05
)

#Figure
bc012<-ggplot(datPlacebo012, aes(y=Values, x=Visit, colour=Visit)) + 
  geom_violin(aes(fill=Visit,colour=NULL))+
  geom_boxplot(aes(fill=Visit, colour=NULL), 
               width=.1, fill="white", outlier.colour = "black")+
  coord_cartesian(ylim = c(0, 1.15))+
  labs( x=NULL,
        y="Bray-Curtis distance")+
  mynamestheme+
  stat_pvalue_manual(pv012, label = "label",size = 7,tip.length = 0.0)+
  scale_fill_manual(values=c("#fff9ae", "#dab600", "white"))+
  scale_x_discrete(labels=c("Baseline", "Week 48", "Baseline & Week 48"))+
  theme(legend.position = "None")
bc012
```


```{r}
#---------------Placebo baseline and 48 weeks- Aitchison-------------------------------
datPlacebo012A<-read.csv("data/Placebo012pfg_CLR_euclidean1.csv.csv", stringsAsFactors = TRUE, row.names = 1)
colnames(datPlacebo012A)
str(datPlacebo012A)
summary(datPlacebo012A)


#Manually assigning p values
pv012A <- data.frame(
  group1 = "0m",
  group2 = "Bewtween_group_matrix",
  label = "*p = 0.66",
  y.position = 138
)

#Figure
bc012A<-ggplot(datPlacebo012A, aes(y=Values, x=Visit, colour=Visit)) + 
  geom_violin(aes(fill=Visit,colour=NULL))+
  geom_boxplot(aes(fill=Visit, colour=NULL), 
               width=.1, fill="white", outlier.colour = "black")+
  coord_cartesian(ylim = c(0, 150))+
  labs( x=NULL,
        y="Aitchison distance")+
  mynamestheme+
  stat_pvalue_manual(pv012A, label = "label",size = 7, tip.length = 0.0)+
  scale_fill_manual(values=c("#fff9ae", "#dab600", "white"))+
  scale_x_discrete(labels=c("Baseline", "Week 48", "Baseline & Week 48"))+
  theme(legend.position = "None")
bc012A


#Combine the two plots
m012<-cowplot::plot_grid(bc012A, bc012, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c("A", "B"), hjust =-0.9,  label_size = 22, label_fontfamily = "Helvetica", label_fontface = "bold", label_colour = "dark blue")
m012

ggsave("Month012Placebo_beta13thDec2021.pdf", m012,  width = 40, height = 25, units = "cm")

```


#Week 48 and 72- Placebo only
```{r}

#scale_fill_manual(values=c("#fff9ae", "#dab600", "#46019B"))

#-----------------Placebo 48 and 72 weeks-Bray-Curtis-----------------------
datPlacebo1218<-read.csv("data/Placebo1218pfg_bray1.csv.csv", stringsAsFactors = TRUE, row.names = 1)
colnames(datPlacebo1218)
str(datPlacebo1218)
summary(datPlacebo1218)


#Manually assigning p values
pv1218 <- data.frame(
  group1 = "12m",
  group2 = "Bewtween_group_matrix",
  label = "*p = 0.51",
  y.position = 1.05
)

#Figure
bc1218<-ggplot(datPlacebo1218, aes(y=Values, x=Visit, colour=Visit)) + 
  geom_violin(aes(fill=Visit,colour=NULL))+
  geom_boxplot(aes(fill=Visit, colour=NULL), 
               width=.1, fill="white", outlier.colour = "black")+
  coord_cartesian(ylim = c(0, 1.15))+
  labs( x=NULL,
        y="Bray-Curtis distance")+
  mynamestheme+
  stat_pvalue_manual(pv1218, label = "label",size = 7, tip.length = 0.0)+
  scale_fill_manual(values=c("#dab600", "#554904", "white"))+
  scale_x_discrete(labels=c("Week 48", "Week 72","Week 48 & 72" ))+
  theme(legend.position = "None")
bc1218


#---------------Placebo 48 and 72 weeks- Aitchison-------------------------------
datPlacebo1218A<-read.csv("data/Placebo1218pfg_CLR_euclidean1.csv.csv", stringsAsFactors = TRUE, row.names = 1)
colnames(datPlacebo1218A)
str(datPlacebo1218A)
summary(datPlacebo1218A)


#Manually assigning p values
pv1218A <- data.frame(
  group1 = "12m",
  group2 = "Bewtween_group_matrix",
  label = "*p = 0.70",
  y.position = 138
)

#Figure
bc1218A<-ggplot(datPlacebo1218A, aes(y=Values, x=Visit, colour=Visit)) + 
  geom_violin(aes(fill=Visit,colour=NULL))+
  geom_boxplot(aes(fill=Visit, colour=NULL), 
               width=.1, fill="white", outlier.colour = "black")+
  coord_cartesian(ylim = c(0, 150))+
  labs( x=NULL,
        y="Aitchison distance")+
  mynamestheme+
  stat_pvalue_manual(pv1218A, label = "label",size = 7, tip.length = 0.0)+
  scale_fill_manual(values=c("#dab600", "#554904", "white"))+
  scale_x_discrete(labels=c("Week 48", "Week 72", "Week 48 & 72"))+
  theme(legend.position = "None")
bc1218A


#Combine the two plots
m1218<-cowplot::plot_grid(bc1218A, bc1218, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c("C", "D"), hjust =-0.9,  label_size = 22, label_fontfamily = "Helvetica", label_fontface = "bold", label_colour = "dark blue")
m1218

ggsave("Month1218Placebo_beta13thDec2021.pdf", m1218,  width = 40, height = 25, units = "cm")



```

```{r}

#scale_fill_manual(values=c("#fff9ae", "#dab600", "#46019B"))

#-----------------Placebo 48 and 72 weeks-Bray-Curtis-----------------------
datPlacebo018<-read.csv("data/Placebo018pfg_bray1.csv.csv", stringsAsFactors = TRUE, row.names = 1)
colnames(datPlacebo018)
str(datPlacebo018)
summary(datPlacebo018)


#Manually assigning p values
pv018 <- data.frame(
  group1 = "0m",
  group2 = "Bewtween_group_matrix",
  label = "*p = 0.51",
  y.position = 1.05
)

#Figure
bc018<-ggplot(datPlacebo018, aes(y=Values, x=Visit, colour=Visit)) + 
  geom_violin(aes(fill=Visit,colour=NULL))+
  geom_boxplot(aes(fill=Visit, colour=NULL), 
               width=.1, fill="white", outlier.colour = "black")+
  coord_cartesian(ylim = c(0, 1.15))+
  labs( x=NULL,
        y="Bray-Curtis distance")+
  mynamestheme+
  stat_pvalue_manual(pv018, label = "label",size = 7, tip.length = 0.0)+
  scale_fill_manual(values=c("#fff9ae", "#554904", "white"))+
  scale_x_discrete(labels=c("Baseline", "Week 72", "Baseline & Week 72"))+
  theme(legend.position = "None")
bc018


#---------------Placebo 48 and 72 weeks- Aitchison-------------------------------
datPlacebo018A<-read.csv("data/Placebo018pfg_CLR_euclidean1.csv.csv", stringsAsFactors = TRUE, row.names = 1)
colnames(datPlacebo018A)
str(datPlacebo018A)
summary(datPlacebo018A)


#Manually assigning p values
pv018A <- data.frame(
  group1 = "0m",
  group2 = "Bewtween_group_matrix",
  label = "*p = 0.51",
  y.position = 138
)

#Figure
bc018A<-ggplot(datPlacebo018A, aes(y=Values, x=Visit, colour=Visit)) + 
  geom_violin(aes(fill=Visit,colour=NULL))+
  geom_boxplot(aes(fill=Visit, colour=NULL), 
               width=.1, fill="white", outlier.colour = "black")+
  coord_cartesian(ylim = c(0, 150))+
  labs( x=NULL,
        y="Aitchison distance")+
  mynamestheme+
  stat_pvalue_manual(pv018A, label = "label",size = 7, tip.length = 0.0)+
  scale_fill_manual(values=c("#fff9ae", "#554904", "white"))+
  scale_x_discrete(labels=c("Baseline", "Week 72", "Baseline & Week 72"))+
  theme(legend.position = "None")
bc018A

#Combine the two plots
m018<-cowplot::plot_grid(bc018A, bc018, nrow = 1, ncol = 2, scale = .9, vjust=1.5, labels = c("E", "F"), hjust =-0.9,  label_size = 22, label_fontfamily = "Helvetica", label_fontface = "bold", label_colour = "dark blue")
m018

ggsave("Month018Placebo_beta13thDec2021.pdf", m018,  width = 40, height = 25, units = "cm")


```


#Combine all- Placebo only

```{r}
all_Placebo<-cowplot::plot_grid(m012, m1218, m018, nrow = 3, ncol = 1, scale = .9, vjust=c(1.1,1.2,1.2), hjust=c(-1.25,-1.69,-1.25), labels = c("Baseline vs Week 48 (Placebo)", "Week 48 vs 72 (Placebo)", "Baseline vs Week 72 (Placebo)"), label_size = 30,label_fontfamily = "Helvetica")
all_Placebo


ggsave("Placebo_ONLY_all_labels13thDec2021.pdf", all_Placebo,  width = 50, height = 50, units = "cm")
```
















